Cancer is the second leading cause of deaths in the US. According to the American Cancer Society, 1.6 million new cancer cases will be diagnosed and about 600,000 people will die from the disease in 2012. Radiation therapy is a major modality for treating cancer patients. Moreover, lung cancer is the leading cause of cancer death in the United States among every ethnic group. It accounts for about 12% of all newly diagnosed cancers, and about 29% of all cancer deaths. Every day, approximately 439 Americans die from lung cancer. In fact, more people die from lung cancer each year than breast, prostate, colon, liver, kidney and melanoma cancers combined. The treatment outcome of the current modalities has been poor; the 5-year overall survival rate for lung cancer is about 15%.
Studies have shown that an increased radiation dose to the tumor will lead to improved local control and survival rates. However, in many anatomic sites (e.g., lung and liver), the tumors can move significantly (˜2-3 cm) with respiration. The respiratory tumor motion has been a major challenge in radiotherapy to deliver sufficient radiation dose without causing secondary cancer or severe radiation damage to the surrounding healthy tissue [27, 28].
Motion-adaptive radiotherapy explicitly accounts for and tackles the issue of tumor motion during radiation dose delivery, in which respiratory-gating and tumor tracking are two promising approaches. Respiratory gating limits radiation exposure to a portion of the breathing cycle when the tumor is in a predefined gating window [29]. Tumor tracking, on the other hand, allows continuous radiation dose delivery by dynamically adjusting the radiation beam so that it follows the real-time tumor movement. For either technique to be effective, accurate measurement of the respiration signal is required. Conventional methods for respiration measurement are undesirable because they are either invasive to the patient or do not have sufficient accuracy.
For instance, measurement based on fiducial markers requires an invasive implantation procedure and involves serious risks to the patient, e.g., pneumothorax for lung cancer patients [30]. These can be either radiopaque metal markers tracked fluoroscopically [2]-[4] or small wireless transponders tracked using non-ionizing electromagnetic fields [5]. While the accuracy of marker tracking is clinically sufficient (˜2 mm) [6], the implantation procedure is invasive and may cause various side effects such as pneumothorax, bleeding/lung collapse, infections, respiratory failure requiring ventilator support, vasovagal reaction causing cardiac arrhythmias and even death in rare circumstance [7, 8]. These risks have greatly limited the acceptance of marker based tracking in lung cancer radiotherapy.
In markerless tracking, lung tumors are tracked in fluoroscopy without implanted fiducial markers based on computer vision [9, 10, 11] or machine learning techniques [12]. A major drawback with these techniques is that they require separate fluoroscopic images be acquired and analyzed by clinicians prior to each treatment fraction, which makes it difficult to be implemented in clinical routine. Another issue with tumor tracking using fluoroscopy is the imaging dose. The main risk for prolonged thoracic fluoroscopy is skin burns [13]. The entrance skin dose rate is about 10 mGy/min for typical fluoroscopic parameters used for thoracic imaging, which amounts to 300 mGy for 30 min beam on time. Beside issues like skin burns, the additional imaging dose may also induce secondary cancer or genetic defects [14].
An alternative to localizing tumors directly in fluoroscopy is through external respiratory surrogates, such as patient surface or tidal volume [15]. Kubo et al. developed a respiratory monitoring system that tracks infrared reflective markers placed on the patient's abdomen surface using a video camera [16]. Another commonly used device to monitor respiration is the spirometer [17], which measures the time-integrated air flow and provides the lung volume information from a baseline (e.g., end of exhale). Siemens Medical Systems has a monitoring interface that receives the respiratory signal from a pressure cell on a belt around the patient that senses pressure changes as the patient breathes. All these respiratory monitoring devices give a 1-dimensional output, and effectively measure the breathing signal from a single point. They do not provide any information beyond the point of measurement. For some complicated breathing patterns, this information may not be sufficient to derive the tumor location accurately. More recently, 3D surface imaging systems using video cameras are able to obtain surface images and monitor the patient's surface in real time [18]. Although this technique is noncontact, the reconstructed surface images are sensitive to ambient lighting as well as the clothing around the patient, which can be problematic for the purpose of tumor tracking.
The measurement of external respiration surrogates using infrared reflective marker, spirometer, or pressure belt etc., generally lacks sufficient accuracy to infer the internal tumor position, because they only provide a point measurement or a numerical index of the respiration [31]. In addition, these devices have to be in close contact with the patient in order to function. This often brings discomfort to the patient and can lead to additional patient motion during dose delivery. To that end, accurate respiration measurement which does not require invasive procedures or patient contact is urgently needed in order to realize the potential of motion-adaptive radiotherapy.
Continuous-wave (CW) radar sensor provides a non-contact and non-invasive approach for respiration measurement [19, 20, 21, 32, 33]. Instead of measuring the marker, it directly measures the periodic motion of the body (e.g., breathing and heartbeat), which has better correlation with the lung tumor motion. Moreover, the radar system is insensitive to clothing and chest hair, due to microwave penetration, making it better than the existing contact devices that are sensitive to the surrounding environment. For example, a quadrature Doppler radar has been described in [42]. A fast solution to build a vital sign radar in ordinary laboratories was presented in [22]. Although many results were demonstrated using bench-top prototypes or board-level integration, the potential of being integrated on a small semiconductor chip was also demonstrated [23].
In radar respiration measurement, the radar sensor suffers from DC offset at the RF front-end output, which is mainly caused by the reflections from stationary objects surrounding the body. The DC offset may saturate or limit the dynamic range of the following stages of baseband amplifiers. To overcome this demerit, AC coupling has been commonly used in radar sensors. However, due to the high-pass characteristics of the coupling capacitor, AC coupling leads to significant signal distortion when the target motion has a very low frequency or a DC component. Respiration is such a motion that is low frequency of less than 0.5 Hz, and tends to rest for a while at the end of expiration, i.e., there is a short stationary moment after lung deflation. This is a problem in radar respiration measurement. To deal with it, several approaches, such as high RF-LO isolation mixers [34], have been introduced to employ DC coupling in radar sensors. However, these approaches are either cumbersome to implement or do not completely remove DC offsets and limit the dynamic range of the baseband amplifiers.